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Special Issue Editors

Special Issue Information

Dear Colleagues,

The Data mining for the Analysis of Performance and Success workshop will be held on the 18th of November 2017, in New Orleans (USA). Though this is the second edition of the workshop, the research community is positively reacting to the emerging challenges related to the so-called Science of Success. With this Special Issue we look forward to summarizing remarkable contributions in big data tools for performance analytics, predictive models for success, analysis of collective success, well-being, and development, made by the academic community attending to DAPS.

Dr. Paolo Cintia
Dr. Alessio Rossi
Dr. Luca Pappalardo
Prof. Roberta Sinatra
Guest Editors

Manuscript Submission Information

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Keywords

Data mining
Data Science
Performance analytics
Science of success

Published Papers (1 paper)

Research

18 pages, 874 KiB

Open AccessArticle

Non-Negative Tensor Factorization for Human Behavioral Pattern Mining in Online Games

by Anna Sapienza, Alessandro Bessi and Emilio Ferrara

Information 2018, 9(3), 66; https://doi.org/10.3390/info9030066 - 16 Mar 2018

Cited by 27 | Viewed by 6455

Abstract

Multiplayer online battle arena is a genre of online games that has become extremely popular. Due to their success, these games also drew the attention of our research community, because they provide a wealth of information about human online interactions and behaviors. A crucial problem is the extraction of activity patterns that characterize this type of data, in an interpretable way. Here, we leverage the Non-negative Tensor Factorization to detect hidden correlated behaviors of playing in a well-known game: League of Legends. To this aim, we collect the entire gaming history of a group of about 1000 players, which accounts for roughly 100K matches. By applying our framework we are able to separate players into different groups. We show that each group exhibits similar features and playing strategies, as well as similar temporal trajectories, i.e., behavioral progressions over the course of their gaming history. We surprisingly discover that playing strategies are stable over time and we provide an explanation for this observation. Full article

(This article belongs to the Special Issue Data Mining for the Analysis of Performance and Success)

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